Abstract 5409: NuKit: a deep learning platform for histopathological Images
Authors: Ching-Nung Lin, Christine H. Chung, Aik Choon Tan
Journal: Cancer Research (2023)

Abstract Hematoxylin and eosin (H&amp;E) staining is the most common type of histopathological images used for quantitative data analysis. For histopathological image analysis, nucleus segmentation represents one of the initial steps in quantitative data analysis pipelines. Recently, deep learning methods for nucleus segmentation on histopathological images become the mainstream in digital pathology. However, adopting these pre-trained models in digital pathology or clinical research remains limited. Two main factors limit the usage of deep learning methods in routine research: 1) many models required some technical background to execute the programs, and 2) the speed of returning the results to the users. To overcome these limitations, we have developed and implemented NuKit, a deep learning platform which accelerates nucleus segmentation and provides prompt results to the users. NuKit platform consists of two deep learning models coupled with an interactive graphical user interface (GUI) to provide fast and automatic nucleus segmentation “on the fly”. The two deep learning models are: 1) the whole image segmentation model and 2) the click segmentation model. Both deep learning models provide complementary tasks in nucleus segmentation in the NuKit platform. The whole image segmentation model performs whole image nucleus whereas the click segmentation model supplements the nucleus segmentation with user-driven input to edits the segmented nuclei. We used PanNuke as the training data set which contains 160,368 nuclei extracted from 7,901 image tiles. The sources of this data set were curated from The Cancer Genome Atlas (TCGA) and included a very few in-house data. For test sets, we used several popular data sets such as TCGA (not used in the training set). We used DICE coefficient as the metric for comparisons of NuKit with other deep learning methods. For the results, we observed that NuKit achieved comparable results with the state-of-the-art deep learning methods and outperforms those non-deep learning methods. For TNBC and CoNSeP data sets, NuKit achieved DICE 0.793 and 0.854 comparing to HoVer-Net 0.749 and 0.664. For CoNSeP test data set, NuKit achieved DICE 0.860 comparing to Cell Profiler 0.434 and QuPath 0.588. In addition, both pre-trained models were embedded in the NuKit GUI, which provides interactive and prompt response to users in analyzing their histopathological images. The outputs of NuKit are interoperable with other quantitative image analysis tools to facilitate the ecosystem of computational pathology to many formats to combine with other software to construct pipelines. In summary, we have introduced NuKit, an innovation platform which combines two deep learning models coupled with interactive GUI to accelerate nucleus segmentation task in histopathological images. We believe that NuKit provides a new platform to bridge the pre-trained deep learning models into digital pathology and clinical usage. Citation Format: Ching-Nung Lin, Christine H. Chung, Aik Choon Tan. NuKit: a deep learning platform for histopathological Images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5409.